Unsupervised (parameter) learning for MRFs on bipartite graphs
Tomas Sixta
(CTU Prague, Czech Republic)
Abstract:
We consider unsupervised (parameter) learning for general Markov random
fields on bipartite graphs. This model class includes Restricted
Boltzmann Machines. We show that besides the stochastic gradient
approximation (a.k.a. persistent contrastive divergence) there is an
alternative learning approach – a modified EM algorithm which is
tractable because of the bipartiteness of the model graph. We compare
the resulting double loop algorithm and the persistent contrastive
divergence learning experimentally and show that the former converges
faster and more stable than the latter.